1. Field
One or more exemplary embodiments relate to apparatuses and methods of resampling event data for quantitative improvement of a positron emission tomography (PET) image, and more particularly, to apparatuses and methods of resampling event data for quantitative improvement of a PET image, which acquires a quantitatively-improved new PET image by analyzing a storage format of list data prior to conversion into the PET image and nonparametrically resampling event data from the list data based on the analysis results in order to improve noise and statistical characteristics thereof.
2. Description of the Related Art
Nuclear medicine imaging and magnetic resonance imaging (MRI) have been complementarily used for focus detection and disease diagnosis, and recently, analysis methods using new data and merged image processing and multimodal imaging have been developed due to an environmental change such as integration of diagnosis and treatment fields using positron emission tomography (PET) and diffusion weighted-MRI (DW-MRI).
Despite the integration of hardware, multimodal imaging degrades a matching rate due to a lack of a count rate and a mismatch between images due to a difference between acquisition methods of machines. An algorithm for matching between multimodal images intends to remove a mismatch between images by improving a matching rate by using a gradient difference (GD) based on a derivative, a Kullback Leibler distance (KLD) for measurement of the entropy of a discrete probability distribution, and normalized mutual information (NMI) based on information and entropy of an image. Also, a data resampling technique is used to increase a count rate to improve a matching rate between a nuclear medicine image and a DW-MRI image.
In a diagnostic MRI field, Cohen-Adad et al. have introduced a method that rearranges high angular resolution diffusion imaging (HARDI) data by jackknife sampling and regular bootstrap in order to evaluate the quality of HARDI based on Q-Ball imaging (QBI) reconstruction, measures an orientation distribution function (ODF) in each piece of bootstrap data, and performs evaluation based on a change in a b-value determining an echo time and a diffusion degree of tissue.
In a gated MRI image, in order to improve image quality, an EPI image has been acquired based on the existence and nonexistence of heart gating, and the uncertainty of a diffusion tensor image (DTI) with derived parameters has been measured and quantized by residual bootstrap.
In gated PET data, image quality is degraded due to a low count rate and a disease detection capability is degraded due to increased noise, which obstructs quantitative improvement. Also, the low sensitivity of single-photon emission computed tomography (SPECT) data amplifies noise; however, when a filtering technique is used to remove such noise, resolution is degraded. The image quality may be improved when a bootstrap data resampling technique is used in order to overcome this problem.
Huang et al. have intended to improve a diagnosis performance by performing a bootstrap based on sinogram data in whole-body PET imaging to identify a portion influenced by motion and evaluate noise. Buvat has presented a protocol that may improve the quality of PET/SPECT images by using a nonparametric bootstrap method in order to measure the statistical characteristics of a reconstruction algorithm and projection data. Kukreja and Gunn have introduced that a bootstrap method for estimating a parameter error in dynamic PET data may calculate a parameter error based on a parametric image or a region or interest (ROI).
Groiselle and Glick have introduced a method that performs a simulation by constructing 20 data sets by using a bootstrap technique in order to evaluate noise in 3D OSEM list-mode iterative reconstruction, and evaluates noise by sampling an event of a list data set having the same size.
A bootstrap method for reducing count rate degradation in the acquisition of a nuclear medicine image and an MRI image is difficult to use in clinical stages due to a lot of data processing. However, since efforts have recently been made to use the bootstrap method in the clinical stages, due to an improvement in computer performance and an improvement in algorithms, a technology implementing the bootstrap method is required.